Job-Ready Skills for the Real World
A Practical Guide to Building, Automating, and Scaling Machine Learning Pipelines with Modern Tools and Best Practices
What you will learn
Understand the core concepts, benefits, and evolution of MLOps.
Learn the differences between MLOps and DevOps practices.
Set up a version-controlled MLOps project using Git and Docker.
Build end-to-end ML pipelines from data preprocessing to deployment.
Transition ML models from experimentation to production environments.
Deploy and monitor ML models for performance and data drift.
Gain hands-on experience with Docker for ML model containerization.
Learn Kubernetes basics and orchestrate ML workloads effectively.
Set up local and cloud-based MLOps infrastructure (AWS, GCP, Azure).Troubleshoot common challenges in scalability, reproducibility, and reliability.
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